Data Structure in R: XTS
Somsak Chanaim
International College of Digital Innovation, CMU
August 7, 2025
Warning
In this chapter, we cannot use the quantmod package because the technology is still under development.”
Why we need quantmod
Download daily price of AAPL from Yahoo finance, from 1 Jan 2020 to 30 Sep 2024, and upload to Rstudio and make this plot
Quantitative Financial Modelling Framework (quantmod)
The quantmod is useful for quantitative financial modelling framework and have three goals:
download data
charting
technical indicator
Download Data
getSymbols() function
getSymbols: The function for load and manage data from multiple source
Recommend the arguments
Symbols : vector of stock or asset name
src : “yahoo”, FRED” , etc (this course use yahoo)
periodicity : “daily”, “weekly”, or monthly”
from : Starting day: Year-month-day (2020-11-31)
to : Ending day: Year-month-day (2022-12-31)
The object from yahoo finance is XTS.
Example
or download multiple stocks price
The xts package
eXtensible Time Series (xts)
eXtensible Time Series (xts) is a powerful package that provides an extensible time series class, enabling uniform handling of many R time series classes by extending zoo.
The main benefit of using xts is the compatibility with other packages that use different time-series classes (timeSeries, zoo, …).
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library(xts)
_webr_editor_2 = Object {code: "library(xts)", options: Object, indicator: it}
Selecting, Subsetting and Indexing
We have 3 xts objects, AAPL, META, and MSFT
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load("file/Data.RData")
ls()
_webr_editor_3 = Object {code: "load(\"file/Data.RData\")\nls()", options: Object, indicator: it}
Data Frame command
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head(AAPL)
_webr_editor_4 = Object {code: null, options: Object, indicator: it}
Warning
In this study, We don’t need the variables AAPL.Volume and AAPL.Adjusted
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AAPL <- AAPL[, 1:4]
head(AAPL)
_webr_editor_5 = Object {code: null, options: Object, indicator: it}
Selecting data by year
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AAPL["2023"]
_webr_editor_6 = Object {code: null, options: Object, indicator: it}
Selecting data by year and month
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# 2022-12/2023-01
AAPL["2022-12/2023-01"]
_webr_editor_7 = Object {code: null, options: Object, indicator: it}
Selecting data from starting date to any date
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# start to 2022-06-05
AAPL["/2022-06-05"]
_webr_editor_8 = Object {code: null, options: Object, indicator: it}
Selecting data from any date to last date
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# 2023-02 to last date
AAPL["2023-02/"]
_webr_editor_9 = Object {code: null, options: Object, indicator: it}
Selecting data for the first n weeks
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# select the first week
first(AAPL,"1 week")
_webr_editor_10 = Object {code: null, options: Object, indicator: it}
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# select the first 3 weeks
first(AAPL,"3 week")
_webr_editor_11 = Object {code: null, options: Object, indicator: it}
Selecting data for the first n days
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# select the first day
first(AAPL,"1 day")
_webr_editor_12 = Object {code: null, options: Object, indicator: it}
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# select the first 3 days
first(AAPL,"3 day")
_webr_editor_13 = Object {code: null, options: Object, indicator: it}
Selecting data for the last n weeks
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# select the last week
last(AAPL,"1 week")
_webr_editor_14 = Object {code: null, options: Object, indicator: it}
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# select the last 2 weeks
last(AAPL,"2 week")
_webr_editor_15 = Object {code: null, options: Object, indicator: it}
Selecting data for the last n days
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# select the last day
last(AAPL,"1 day")
_webr_editor_16 = Object {code: null, options: Object, indicator: it}
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# select the last 3 days
last(AAPL,"3 day")
_webr_editor_17 = Object {code: null, options: Object, indicator: it}
From daily price to weekly price
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to.weekly(AAPL)
_webr_editor_18 = Object {code: null, options: Object, indicator: it}
From daily price to monthly price
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to.monthly(AAPL)
_webr_editor_19 = Object {code: null, options: Object, indicator: it}
From daily price to monthly price
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to.quarterly(AAPL)
_webr_editor_20 = Object {code: null, options: Object, indicator: it}
How to export XTS object to xlsx file
Case 1: Export only one xts object to an .xlsx file.
Step 1: Convert the xts object to a data frame.
coredata()extracts the data values from the xts object, excluding the time index.as.data.frame()converts the extracted data into a standard data frame.DF_AAPL$Date <- index(AAPL)adds a new column called Date, containing the time index from the original xts object.
Step 2: Save data frame DF_AAPto xlsx
Case 2: Export multiple xts objects to an .xlsx file.
Step 1: Convert the xts object to a data frame.
Step 2: Save every data frame to xlsx
How to Import Time Series Data from an XLSX File into an XTS Object in R
You can download financial time series data from my webapp.
Assume we have excel file file.xlsx
library(readxl)
# TS = time series data frame
TS <- read_excel("file.xlsx",
col_types = c("date", "numeric", "numeric",
"numeric", "numeric", "numeric",
"numeric"))
str(TS)tibble [968 × 7] (S3: tbl_df/tbl/data.frame)
$ date : POSIXct[1:968], format: "2021-01-04" "2021-01-05" ...
$ PTT.BK.Open : num [1:968] 41 42.2 43 43 42.8 ...
$ PTT.BK.High : num [1:968] 42.8 42.5 43.5 43.2 43.5 ...
$ PTT.BK.Low : num [1:968] 40.8 41.2 42.5 42.2 42 ...
$ PTT.BK.Close : num [1:968] 42.5 42 42.8 42.5 42.8 ...
$ PTT.BK.Volume : num [1:968] 74638600 75867500 88565400 63128900 83698400 ...
$ PTT.BK.Adjusted: num [1:968] 33 32.6 33.1 33 33.1 ...Convert the data frame TS to an XTS object using this function.
library(xts)
TS <- xts(x = TS[,2:6],
order.by = TS$date)
first(TS, n = 7) PTT.BK.Open PTT.BK.High PTT.BK.Low PTT.BK.Close PTT.BK.Volume
2021-01-04 41.00 42.75 40.75 42.50 74638600
2021-01-05 42.25 42.50 41.25 42.00 75867500
2021-01-06 43.00 43.50 42.50 42.75 88565400
2021-01-07 43.00 43.25 42.25 42.50 63128900
2021-01-08 42.75 43.50 42.00 42.75 83698400
2021-01-11 42.75 43.00 42.50 43.00 45553200
2021-01-12 43.00 44.00 42.50 43.75 101081900Exercise
Exercise 1: Extract Data for a Specific Year
Extract and display the stock data for “MSFT” for the year 2021. Use the xts package’s subsetting techniques to filter by date.
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_webr_editor_23 = Object {code: null, options: Object, indicator: it}
Exercise 2: Filter Data Between Two Dates
Subset the “META” data to include only the observations between January 1, 2022, and December 31, 2022.
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_webr_editor_24 = Object {code: null, options: Object, indicator: it}
Exercise 3: Select Data for the First Quarter
Extract the data for “MSFT” for the first quarter of 2023 (January to March) and display the results.
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_webr_editor_25 = Object {code: null, options: Object, indicator: it}
Exercise 4: Subset Data for the First N Weeks
Create a subset of “META” data for the first 10 weeks of 2024 and calculate the average closing price during that period.
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_webr_editor_26 = Object {code: null, options: Object, indicator: it}
Exercise 5: Compare Data Over Two Periods
Extract “MSFT” data for the first 6 months of 2021 and 2023, and compare the average closing prices for these two periods.
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_webr_editor_27 = Object {code: null, options: Object, indicator: it}
Exercise 6: Select the Last N Observations
Subset the “META” data to get the last 8 weeks of observations before September 30, 2024.
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_webr_editor_28 = Object {code: null, options: Object, indicator: it}
Downloading webR
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Data Structure in R: XTS Somsak Chanaim International College of Digital Innovation, CMU August 7, 2025